{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/variable-prioritization-in-nonlinear-black","title":"Variable Prioritization in Nonlinear Black Box Methods: A Genetic Association Case Study","arxiv_id":"1801.07318","date":"2018-01-22","proceeding":null,"authors":["Lorin Crawford","Seth R. Flaxman","Daniel E. Runcie","Mike West"],"abstract":"The central aim in this paper is to address variable selection questions in\nnonlinear and nonparametric regression. Motivated by statistical genetics,\nwhere nonlinear interactions are of particular interest, we introduce a novel\nand interpretable way to summarize the relative importance of predictor\nvariables. Methodologically, we develop the \"RelATive cEntrality\" (RATE)\nmeasure to prioritize candidate genetic variants that are not just marginally\nimportant, but whose associations also stem from significant covarying\nrelationships with other variants in the data. We illustrate RATE through\nBayesian Gaussian process regression, but the methodological innovations apply\nto other \"black box\" methods. It is known that nonlinear models often exhibit\ngreater predictive accuracy than linear models, particularly for phenotypes\ngenerated by complex genetic architectures. With detailed simulations and two\nreal data association mapping studies, we show that applying RATE enables an\nexplanation for this improved performance.","url_abs":"http://arxiv.org/abs/1801.07318v3","url_pdf":"http://arxiv.org/pdf/1801.07318v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"variable-prioritization-in-nonlinear-black","repo_url":"https://github.com/lorinanthony/RATE","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"variable-selection","task_name":"Variable Selection"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[{"method_slug":"gaussian-process","method_name":"Gaussian Process"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}